WEGAN: a web-based community ecology platform
Bibliographic record
Abstract
Community ecology studies how species interact in their ecosystems, influenced by environmental and phenotypic factors. Analyzing these complex interactions requires specialized software or proficiency in statistical programming. While many stand-alone community ecology software tools exist, there is a gap for a free and widely available webserver to support community ecology analysis. To address this shortcoming we have developed WEGAN (Web-based Ecological Group Analysis), an easy-to-use webserver for analyzing and visualizing community ecology data. WEGAN is designed to provide features offered by popular programs such as vegan through a point-and-click web interface. Specifically, WEGAN provides a wide range of community ecology methods to support the analysis and visualization of trends in dispersal, diversity, and taxonomy as well as univariate and multivariate statistics for clustering, classification, correlation, and ordination analysis. WEGAN offers intuitive workflows and generates detailed tables, publication quality figures and a complete (reproducible) R coding history of all inputs, operations and outputs for every user session, together with comprehensive tutorials. WEGAN was developed to help with the teaching and training of community ecology and to encourage wider use of sophisticated community ecology techniques. WEGAN is freely available at https://www.wegan.ca .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".